SYNLAB Italy
Bioinformatic and Biostatistic Unit, SYNLAB IRCCS SDN, Naples
An Integrative Computational Approach Based on Expression Similarity Signatures to Identify Protein-Protein Interaction Networks in Female-Specific Cancers
Frontiers in Genetics – https://doi.org/10.3389/fgene.2020.612521
What inspired your research and what did it cover?
Breast, ovarian, and endometrial cancers have a major impact on women’s mortality and share estrogen-dependent mechanisms supporting tumor growth differently. The study covers the need of delivering more precision medicine in women's health. We integrated public next-generation sequencing (NGS / transcriptomes) data of the three complex diseases to decipher common protein-protein interaction networks underlying these female-specific cancers.
Which aspect(s) of your research work are you particularly excited about?
I believe that this paper could be a pilot study aiming at establishing the co-occurrence of gene networks underlying breast, endometrial and ovarian cancers, i.e., clinically distinct entities. I am particularly excited about the ERBB2 centered protein-protein interaction network that might impact endogenous estrogen-dependent signaling.
Looking at the potential of your findings, what difference can they make?
In line with the SYNLAB diagnostic innovations mission, these findings will have a remarkable value for medical science/personalized medicine. They might open the way to novel molecular targets for the development of female-specific oncogenic panels. They might also provide added value to customers by promoting women’s health screening programs.
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